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feat(server): reduce memory requirement (#214)

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Nick Hill 2023-04-24 05:15:42 -07:00 committed by GitHub
parent 6ded76a4ae
commit 4a7dd4085a
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5 changed files with 278 additions and 165 deletions
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23
server/tests/models/test_bloom.py
View File

@ -175,12 +175,14 @@ def test_causal_lm_generate_token_completion_multi(
generations[1].generated_text.generated_tokens
== default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
)
# Copy stopping_criterias before filtering
stopping_criterias = default_multi_requests_bloom_batch.stopping_criterias.copy()
next_batch = next_batch.filter([next_batch.requests[0]])
for _ in range(
default_multi_requests_bloom_batch.stopping_criterias[0].max_new_tokens
- default_multi_requests_bloom_batch.stopping_criterias[1].max_new_tokens
stopping_criterias[0].max_new_tokens
- stopping_criterias[1].max_new_tokens
- 1
):
generations, next_batch = default_bloom.generate_token(next_batch)
@ -212,6 +214,15 @@ def test_batch_concatenate(
next_batch_1 = default_multi_requests_bloom_batch
_, next_batch_1 = default_bloom.generate_token(next_batch_1)
# Clone past_key_values before concatenating to compare after,
# because they are removed from the concatenated batches
next_batch_0_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_0.past_key_values
]
next_batch_1_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_1.past_key_values
]
next_batch = BloomCausalLMBatch.concatenate([next_batch_0, next_batch_1])
assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
@ -246,15 +257,15 @@ def test_batch_concatenate(
assert all([p[1].shape == (3, 16, 2, 64) for p in next_batch.past_key_values])
for i, past in enumerate(next_batch.past_key_values):
assert torch.equal(next_batch_0.past_key_values[i][0][:, :, -2:], past[0][0])
assert torch.equal(next_batch_0_past_key_values[i][0][:, :, -2:], past[0][0])
assert torch.equal(
next_batch_1.past_key_values[i][0][:, :, -1:],
next_batch_1_past_key_values[i][0][:, :, -1:],
past[0][1:, :, :, -1].reshape(-1, 64, 1),
)
assert torch.equal(next_batch_0.past_key_values[i][1][:, -2:, :], past[1][0])
assert torch.equal(next_batch_0_past_key_values[i][1][:, -2:, :], past[1][0])
assert torch.equal(
next_batch_1.past_key_values[i][1][:, -1:, :],
next_batch_1_past_key_values[i][1][:, -1:, :],
past[1][1:, :, -1, :].reshape(-1, 1, 64),
)

23
server/tests/models/test_causal_lm.py
View File

@ -173,12 +173,14 @@ def test_causal_lm_generate_token_completion_multi(
generations[1].generated_text.generated_tokens
== default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
)
# Copy stopping_criterias before filtering
stopping_criterias = default_multi_requests_causal_lm_batch.stopping_criterias.copy()
next_batch = next_batch.filter([next_batch.requests[0]])
for _ in range(
default_multi_requests_causal_lm_batch.stopping_criterias[0].max_new_tokens
- default_multi_requests_causal_lm_batch.stopping_criterias[1].max_new_tokens
stopping_criterias[0].max_new_tokens
- stopping_criterias[1].max_new_tokens
- 1
):
generations, next_batch = default_causal_lm.generate_token(next_batch)
@ -209,6 +211,15 @@ def test_batch_concatenate(
next_batch_1 = default_multi_requests_causal_lm_batch
_, next_batch_1 = default_causal_lm.generate_token(next_batch_1)
# Clone past_key_values before concatenating to compare after,
# because they are removed from the concatenated batches
next_batch_0_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_0.past_key_values
]
next_batch_1_past_key_values = [
(k.clone(), v.clone()) for (k, v) in next_batch_1.past_key_values
]
next_batch = CausalLMBatch.concatenate([next_batch_0, next_batch_1])
assert torch.equal(next_batch.all_input_ids[0], next_batch_0.all_input_ids[0])
@ -244,14 +255,14 @@ def test_batch_concatenate(
assert all([p[1].shape == (3, 12, 2, 64) for p in next_batch.past_key_values])
for i, past in enumerate(next_batch.past_key_values):
assert torch.equal(next_batch_0.past_key_values[i][0][0, :, -2:], past[0][0])
assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:], past[0][0])
assert torch.equal(
next_batch_1.past_key_values[i][0][:, :, -1:], past[0][1:, :, -1:, :]
next_batch_1_past_key_values[i][0][:, :, -1:], past[0][1:, :, -1:, :]
)
assert torch.equal(next_batch_0.past_key_values[i][1][0, :, -2:], past[1][0])
assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:], past[1][0])
assert torch.equal(
next_batch_1.past_key_values[i][1][:, :, -1:], past[1][1:, :, -1:, :]
next_batch_1_past_key_values[i][1][:, :, -1:], past[1][1:, :, -1:, :]
)
for _ in range(

33
server/tests/models/test_seq2seq_lm.py
View File

@ -219,6 +219,19 @@ def test_batch_concatenate(
next_batch_1 = default_multi_requests_seq2seq_lm_batch
_, next_batch_1 = default_seq2seq_lm.generate_token(next_batch_1)
# Copy hidden state because it is removed from the concatenated branches
next_batch_0_encoder_last_hidden_state = next_batch_0.encoder_last_hidden_state
next_batch_1_encoder_last_hidden_state = next_batch_1.encoder_last_hidden_state
# Clone past_key_values before concatenating to compare after,
# because they are removed from the concatenated batches
next_batch_0_past_key_values = [
[t.clone() for t in layer] for layer in next_batch_0.past_key_values
]
next_batch_1_past_key_values = [
[t.clone() for t in layer] for layer in next_batch_1.past_key_values
]
next_batch = Seq2SeqLMBatch.concatenate([next_batch_0, next_batch_1])
assert next_batch.batch_id == 0
@ -239,11 +252,11 @@ def test_batch_concatenate(
assert torch.equal(
next_batch.encoder_last_hidden_state[0],
next_batch_0.encoder_last_hidden_state[0, -2:],
next_batch_0_encoder_last_hidden_state[0, -2:],
)
assert torch.equal(
next_batch.encoder_last_hidden_state[1:],
next_batch_1.encoder_last_hidden_state[:, -2:],
next_batch_1_encoder_last_hidden_state[:, -2:],
)
assert next_batch.input_lengths == [2, 2, 2]
@ -275,24 +288,24 @@ def test_batch_concatenate(
)
for i, past in enumerate(next_batch.past_key_values):
assert torch.equal(next_batch_0.past_key_values[i][0][0, :, -2:, :], past[0][0])
assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:, :], past[0][0])
assert torch.equal(
next_batch_1.past_key_values[i][0][:, :, -1:, :], past[0][1:, :, -1:, :]
next_batch_1_past_key_values[i][0][:, :, -1:, :], past[0][1:, :, -1:, :]
)
assert torch.equal(next_batch_0.past_key_values[i][1][0, :, -2:, :], past[1][0])
assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:, :], past[1][0])
assert torch.equal(
next_batch_1.past_key_values[i][1][:, :, -1:, :], past[1][1:, :, -1:, :]
next_batch_1_past_key_values[i][1][:, :, -1:, :], past[1][1:, :, -1:, :]
)
assert torch.equal(next_batch_0.past_key_values[i][2][0, :, -2:, :], past[2][0])
assert torch.equal(next_batch_0_past_key_values[i][2][0, :, -2:, :], past[2][0])
assert torch.equal(
next_batch_1.past_key_values[i][2][:, :, -2:, :], past[2][1:]
next_batch_1_past_key_values[i][2][:, :, -2:, :], past[2][1:]
)
assert torch.equal(next_batch_0.past_key_values[i][3][0, :, -2:, :], past[3][0])
assert torch.equal(next_batch_0_past_key_values[i][3][0, :, -2:, :], past[3][0])
assert torch.equal(
next_batch_1.past_key_values[i][3][:, :, -2:, :], past[3][1:]
next_batch_1_past_key_values[i][3][:, :, -2:, :], past[3][1:]
)
for _ in range(3):

189
server/text_generation_server/models/causal_lm.py
View File

@ -150,6 +150,8 @@ class CausalLMBatch(Batch):
next_token_choosers = []
stopping_criterias = []
new_padding_right_offset = 0
for i, r in enumerate(requests):
idx = self.requests_idx_mapping[r.id]
requests_idx_mapping[r.id] = i
@ -164,36 +166,57 @@ class CausalLMBatch(Batch):
max_input_length = max(max_input_length, request_input_length)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criterias.append(self.stopping_criterias[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
new_padding_right_offset = max(
new_padding_right_offset,
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
input_ids = self.input_ids[keep_indices]
attention_mask = self.attention_mask[keep_indices]
position_ids = self.position_ids[keep_indices]
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
past_key_values = [
[t.view(len(self), -1, *t.shape[-2:])[keep_indices] for t in layer]
for layer in self.past_key_values
self.attention_mask = self.attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length):
(self.attention_mask.shape[1] - self.padding_right_offset) + new_padding_right_offset,
]
return CausalLMBatch(
batch_id=self.batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
all_input_ids=all_input_ids,
input_lengths=input_lengths,
offsets=offsets,
token_offsets=token_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
padding_right_offset=self.padding_right_offset,
keys_head_dim_last=self.keys_head_dim_last,
)
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [list(layer) for layer in self.past_key_values]
# Update tensors in-place to allow incremental garbage collection
past_kv_length = max_input_length - 1
for layer in self.past_key_values:
past_keys, past_values = layer
if len(past_keys.shape) == 3:
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
if self.keys_head_dim_last:
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
else:
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
del past_keys
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
del past_values
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = input_ids
self.position_ids = position_ids
self.all_input_ids = all_input_ids
self.input_lengths = input_lengths
self.offsets = offsets
self.token_offsets = token_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
self.padding_right_offset = new_padding_right_offset
return self
@classmethod
@tracer.start_as_current_span("concatenate")
@ -285,62 +308,88 @@ class CausalLMBatch(Batch):
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
position_ids[start_index:end_index] = batch.position_ids
for j, past in enumerate(batch.past_key_values):
past_keys, past_values = past
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
# And ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer] for layer in batch.past_key_values
]
elif batch.past_key_values[0][0].shape == 3:
for layer in batch.past_key_values:
for k, t in enumerate(layer):
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
past_keys = past_keys.view(len(batch), -1, *past_keys.shape[-2:])
past_values = past_values.view(len(batch), -1, *past_values.shape[-2:])
start_index = end_index
_, num_heads, padded_sequence_length, head_dim = past_values.shape
first_past_kvs = batches[0].past_key_values
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
padded_past_values_shape = (
total_batch_size,
num_heads,
max_input_length - 1,
head_dim,
)
padded_past_values_shape = (
total_batch_size,
num_heads,
max_input_length - 1,
head_dim,
)
if batches[0].keys_head_dim_last:
padded_past_keys_shape = padded_past_values_shape
else:
# seq_length is last for BLOOM
padded_past_keys_shape = (
total_batch_size,
num_heads,
head_dim,
max_input_length - 1,
)
# Iterate over attention layers
# Concatenate past key values layer by layer to allow incremental garbage collection
for j in range(len(first_past_kvs)):
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
start_index = 0
for batch in batches:
past_keys = batch.past_key_values[j][0]
# Clear reference to the original tensor
batch.past_key_values[j][0] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the keys to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
if batch.keys_head_dim_last:
padded_past_keys_shape = padded_past_values_shape
padded_past_keys[
start_index:end_index, :, -past_seq_len:, :
] = past_keys[:, :, -past_seq_len:, :]
else:
# seq_length is last for BLOOM
padded_past_keys_shape = (
total_batch_size,
num_heads,
head_dim,
max_input_length - 1,
)
# BLOOM case
padded_past_keys[
start_index:end_index, :, :, -past_seq_len:
] = past_keys[:, :, :, -past_seq_len:]
del past_keys
# This will run only once per layer
if j == len(past_key_values):
padded_past_keys = past_keys.new_zeros(padded_past_keys_shape)
padded_past_values = past_values.new_zeros(padded_past_values_shape)
past_key_values.append((padded_past_keys, padded_past_values))
start_index = end_index
# We slice the past keys and values to remove the padding from previous batches
if batch.keys_head_dim_last:
past_key_values[j][0][
start_index:end_index,
:,
-(batch.max_input_length - 1) :,
:,
] = past_keys[:, :, -(batch.max_input_length - 1) :, :]
else:
past_key_values[j][0][
start_index:end_index,
:,
:,
-(batch.max_input_length - 1) :,
] = past_keys[:, :, :, -(batch.max_input_length - 1) :]
padded_past_values = first_past_kvs[j][1].new_zeros(padded_past_values_shape)
start_index = 0
for batch in batches:
past_values = batch.past_key_values[j][1]
# Clear reference to the original tensor
batch.past_key_values[j][1] = None
past_key_values[j][1][
start_index:end_index, :, -(batch.max_input_length - 1) :, :
] = past_values[:, :, -(batch.max_input_length - 1) :, :]
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past values to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
padded_past_values[
start_index:end_index, :, -past_seq_len:, :
] = past_values[:, :, -past_seq_len:, :]
del past_values
start_index += len(batch)
start_index = end_index
past_key_values.append([padded_past_keys, padded_past_values])
return cls(
batch_id=batches[0].batch_id,

175
server/text_generation_server/models/seq2seq_lm.py
View File

@ -25,7 +25,7 @@ class Seq2SeqLMBatch(Batch):
requests_idx_mapping: Dict[int, int]
# Encoder values
input_ids: torch.Tensor
input_ids: Optional[torch.Tensor]
attention_mask: torch.Tensor
# Decoder values
@ -164,6 +164,7 @@ class Seq2SeqLMBatch(Batch):
max_input_length = 0
max_decoder_input_length = 0
padding_right_offset = 0
for i, r in enumerate(requests):
idx = self.requests_idx_mapping[r.id]
@ -184,45 +185,53 @@ class Seq2SeqLMBatch(Batch):
max_decoder_input_length = max(
max_decoder_input_length, request_decoder_input_length
)
padding_right_offset = max(
padding_right_offset,
self.stopping_criterias[idx].max_new_tokens - self.stopping_criterias[idx].current_tokens
)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criterias.append(self.stopping_criterias[idx])
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
decoder_input_ids = self.decoder_input_ids[keep_indices]
attention_mask = self.attention_mask[keep_indices]
self.decoder_input_ids = self.decoder_input_ids[keep_indices]
self.attention_mask = self.attention_mask[keep_indices, -max_input_length:]
if self.decoder_attention_mask is not None:
decoder_attention_mask = self.decoder_attention_mask[keep_indices]
else:
decoder_attention_mask = None
self.decoder_attention_mask = self.decoder_attention_mask[
keep_indices,
-(self.padding_right_offset + max_decoder_input_length):
(self.decoder_attention_mask.shape[1] - self.padding_right_offset) + padding_right_offset,
]
encoder_last_hidden_state = self.encoder_last_hidden_state[keep_indices]
self.encoder_last_hidden_state = self.encoder_last_hidden_state[keep_indices, -max_input_length:]
past_key_values = [
[t[keep_indices] for t in layer] for layer in self.past_key_values
]
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [[t for t in layer] for layer in self.past_key_values]
decoder_past_seq_len = max_decoder_input_length - 1
for layer in self.past_key_values:
layer[0] = layer[0][keep_indices, :, -decoder_past_seq_len:]
layer[1] = layer[1][keep_indices, :, -decoder_past_seq_len:]
layer[2] = layer[2][keep_indices, :, -max_input_length:]
layer[3] = layer[3][keep_indices, :, -max_input_length:]
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = None
self.all_decoder_input_ids = all_decoder_input_ids
self.input_lengths = input_lengths
self.decoder_input_lengths = decoder_input_lengths
self.offsets = offsets
self.token_offsets = token_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
self.max_decoder_input_length = max_decoder_input_length
self.padding_right_offset = padding_right_offset
return self
return Seq2SeqLMBatch(
batch_id=self.batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=None,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
all_decoder_input_ids=all_decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_last_hidden_state=encoder_last_hidden_state,
past_key_values=past_key_values,
input_lengths=input_lengths,
decoder_input_lengths=decoder_input_lengths,
offsets=offsets,
token_offsets=token_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
max_decoder_input_length=max_decoder_input_length,
padding_right_offset=self.padding_right_offset,
)
@classmethod
@tracer.start_as_current_span("concatenate")
@ -350,58 +359,78 @@ class Seq2SeqLMBatch(Batch):
encoder_last_hidden_state[
start_index:end_index, -batch.max_input_length :, :
] = batch.encoder_last_hidden_state[:, -batch.max_input_length :, :]
batch.encoder_last_hidden_state = None
# Iterate over attention layers
for j, past in enumerate(batch.past_key_values):
_, num_heads, _, head_dim = past[0].shape
# Ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [[t for t in layer] for layer in batch.past_key_values]
# This will run only once per layer
if j == len(past_key_values):
past_key_values.append([])
start_index = end_index
# Decoder past
for k, t in enumerate(past[:2]):
padded_t_shape = (
total_batch_size,
num_heads,
(max_decoder_input_length - 1),
head_dim,
)
# Determine shapes for new past kv tensors
first_past_kvs = batches[0].past_key_values
_, num_heads, _, head_dim = first_past_kvs[0][0].shape
# Initialize tensors
# This will run only once per layer and per past tensor
if k == len(past_key_values[j]):
past_key_values[j].append(t.new_zeros(padded_t_shape))
padded_dec_t_shape = (
total_batch_size,
num_heads,
(max_decoder_input_length - 1),
head_dim,
)
padded_enc_t_shape = (
total_batch_size,
num_heads,
max_input_length,
head_dim,
)
# Iterate over attention layers
for j in range(len(first_past_kvs)):
past_key_values.append([])
# Decoder past
for k in range(0, 2):
# Initialize tensors
padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape)
past_key_values[j].append(padded_past_values)
start_index = 0
for batch in batches:
t = batch.past_key_values[j][k]
# Clear reference to the original tensor
batch.past_key_values[j][k] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past keys and values to remove the padding from previous batches
past_key_values[j][k][
start_index:end_index,
:,
-(batch.max_decoder_input_length - 1) :,
:,
] = t[:, :, -(batch.max_decoder_input_length - 1) :, :]
past_seq_len = batch.max_decoder_input_length - 1
padded_past_values[
start_index:end_index, :, -past_seq_len:, :
] = t[:, :, -past_seq_len:, :]
del t
# encoder past
for k, t in enumerate(past[2:]):
padded_t_shape = (
total_batch_size,
num_heads,
max_input_length,
head_dim,
)
start_index = end_index
idx = k + 2
# Encoder past
for k in range(2, 4):
# Initialize tensors
padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape)
past_key_values[j].append(padded_past_values)
# Initialize tensors
# This will run only once per layer and per past tensor
if idx == len(past_key_values[j]):
past_key_values[j].append(t.new_zeros(padded_t_shape))
start_index = 0
for batch in batches:
t = batch.past_key_values[j][k]
# Clear reference to the original tensor
batch.past_key_values[j][k] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past keys and values to remove the padding from previous batches
padded_past_values[
start_index:end_index, :, -batch.max_input_length:, :
] = t[:, :, -batch.max_input_length:, :]
del t
past_key_values[j][idx][
start_index:end_index, :, -batch.max_input_length :, :
] = t[:, :, -batch.max_input_length :, :]
start_index += len(batch)
start_index = end_index
return cls(
batch_id=batches[0].batch_id,